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24th IEEE International Conference on Image Processing (ICIP)

作者： Zhu, Lin Zhang, Xiang Zhang, Wenju Huang, Xuhui Guan, Naiyang Luo, Zhigang Natl Univ Def Technol Coll Comp Inst Software Changsha Hunan Peoples R China Natl Univ Def Technol Coll Comp Dept Comp Sci & Technol Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781509021758

Domain adaptation (DA) attempts to enhance the generalization capability of classifier through narrowing the gap of the distributions across domains. This paper focuses on unsupervised domain adaptation where labels are not available in target domain. Most existing approaches explore the domain invariant features shared by domains but ignore the discriminative information of source domain. To address this issue, we propose a discriminative domain adaptation method (DDA) to reduce domain shift by seeking a common latent subspace jointly using supervised sparse coding (SSC) and discriminative regularization term. Particularly, DDA adapts SSC to yield discriminative coefficients of target data and further unites with discriminative regularization term to induce a common latent subspace across domains. We show that both strategies can boost the ability of transferring knowledge from source to target domain. Experiments on two real world datasets demonstrate the effectiveness of our proposed method over several existing state-of-the-art domain adaptation methods.

关键词： Transfer learning domain adaptation sparse coding subspace learning

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Dual Graph Regularized sparse coding for Image Representation

Dual Graph Regularized Sparse Coding for Image Representatio...

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IEEE Visual Communications and Image Processing (VCIP)

作者： Sha, Lingdao Schonfeld, Dan Univ Illinois Dept Elect & Comp Engn 851 S Morgan St Chicago IL 60607 USA

ISBN: (纸本)9781538604625

sparse coding has been widely and successfully used in image classification, noise reduction, texture synthesis, and audio processing. Although existing sparse coding methods can produce promising results, they failed to consider the high dimensional manifold information within data. In this paper, we propose a dual graph regularized sparse coding method to effectively preserve duality between data points and features for sparse representation. This is achieved by Feature-sign Search with Lagrange Dual (FS-LD) algorithm and Least-Angle Regression with Block Coordinate Descent (LARS-BCD) algorithm. Experimental results in clustering and classification show that the proposed method outperforms other existing methods.

关键词： sparse coding dual graph regularized sparse coding manifold learning least angle regression feature-sign search algorithm Lagrange dual image clustering block coordinate descent image classification

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sparse coding-based correlaton model for land-use scene classification in high-resolution remote-sensing images

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JOURNAL OF APPLIED REMOTE SENSING 2016年第4期10卷

作者： Qi Kunlun Zhang Xiaochun Wu Baiyan Wu Huayi Wuhan Univ State Key Lab Water Resources & Hydropower Engn S 299 BaYi Rd Wuhan 430072 Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & R 129 Luoyu Rd Wuhan 430079 Peoples R China Hunan Univ Sci & Technol Sch Architecture & Urban Planning Taoyuan Rd Xiangtan 411201 Peoples R China Wuhan Univ Collaborat Innovat Ctr Geospatial Technol 129 Luoyu Rd Wuhan 430079 Peoples R China

High-resolution remote-sensing images are increasingly applied in land-use classification problems. Land-use scenes are often very complex and difficult to represent. Subsequently, the recognition of multiple land-cover classes is a continuing research question. We propose a classification framework based on a sparse coding-based correlaton (termed sparse correlaton) model to solve this challenge. Specifically, a general mapping strategy is presented to label visual words and generate sparse coding-based correlograms, which can exploit the spatial co-occurrences of visual words. A compact spatial representation without loss discrimination is achieved through adaptive vector quantization of correlogram in land-use scene classification. Moreover, instead of using K-means for visual word encoding in the original correlaton model, our proposed sparse correlaton model uses sparse coding to achieve lower reconstruction error. Experiments on a public ground truth image dataset of 21 land-use classes demonstrate that our sparse coding-based correlaton method can improve the performance of land-use scene classification and outperform many existing bag-of-visual-words-based methods. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： sparse coding vector quantization spatial information bag-of-visual-words

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sparse coding for Alpha Matting

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2016年第7期25卷 3032-3043页

作者： Johnson, Jubin Varnousfaderani, Ehsan Shahrian Cholakkal, Hisham Rajan, Deepu Nanyang Technol Univ Sch Comp Sci & Engn Multimedia Lab Singapore 639798 Singapore Univ Calif San Diego Shiley Eye Inst San Diego CA 92093 USA

Existing color sampling-based alpha matting methods use the compositing equation to estimate alpha at a pixel from the pairs of foreground (F) and background (B) samples. The quality of the matte depends on the selected (F, B) pairs. In this paper, the matting problem is reinterpreted as a sparse coding of pixel features, wherein the sum of the codes gives the estimate of the alpha matte from a set of unpaired F and B samples. A non-parametric probabilistic segmentation provides a certainty measure on the pixel belonging to foreground or background, based on which a dictionary is formed for use in sparse coding. By removing the restriction to conform to (F, B) pairs, this method allows for better alpha estimation from multiple F and B samples. The same framework is extended to videos, where the requirement of temporal coherence is handled effectively. Here, the dictionary is formed by samples from multiple frames. A multi-frame graph model, as opposed to a single image as for image matting, is proposed that can be solved efficiently in closed form. Quantitative and qualitative evaluations on a benchmark dataset are provided to show that the proposed method outperforms the current stateoftheart in image and video matting.

关键词： Alpha matting graph model sparse coding

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sparse coding in early visual representation: From specific properties to general principles

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NEUROCOMPUTING 2016年 171卷 1085-1098页

作者： Bruce, Neil D. B. Rahman, Shafin Carrier, Diana Univ Manitoba Dept Comp Sci Winnipeg MB R3T 2N2 Canada

In this paper, we examine the problem of learning sparse representations of visual patterns in the context of artificial and biological vision systems. There are a myriad of strategies for sparse coding that often result in similar feature properties for the learned feature set. Typically this results in a bank of Gabor-like or edge filters that are sensitive to a range of distinct angular and radial frequencies. The theory and experimentation that is presented in this paper serves to provide a better understanding of a number of specific properties related to low-level feature learning. This includes close examination of the role of phase pairing in complex cells, the role of depth information and its relationship to variation of intensity and chroma, and deriving hybrid features that borrow from both analytic forms and statistical methods. Together, these specific examples provide context for more general discussion of effective strategies for feature learning. In particular, we make the case that imposing additional constraints on mechanisms for feature learning inspired by biological vision systems can be useful in guiding constrained optimization towards convergence, or specific desirable computational properties for representation of visual input in artificial vision systems. (C) 2015 Elsevier B.V. All rights reserved.

关键词： sparse coding Feature learning Human vision Gabor filters

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sparse coding joint decision rule for ear print recognition

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OPTICAL ENGINEERING 2016年第9期55卷 093105-1-093105-9页

作者： Guermoui, Mawloud Melaab, Djamel Mekhalfi, Mohamed Lamine Univ Batna Dept Elect Rue Md El Hadi Boukhlouf Batna 05000 Algeria

Human ear recognition has been promoted as a profitable biometric over the past few years. With respect to other modalities, such as the face and iris, that have undergone a significant investigation in the literature, ear pattern is relatively still uncommon. We put forth a sparse coding-induced decision-making for ear recognition. It jointly involves the reconstruction residuals and the respective reconstruction coefficients pertaining to the input features (co-occurrence of adjacent local binary patterns) for a further fusion. We particularly show that combining both components (i.e., the residuals as well as the coefficients) yields better outcomes than the case when either of them is deemed singly. The proposed method has been evaluated on two benchmark datasets, namely IITD1 (125 subject) and IITD2 (221 subjects). The recognition rates of the suggested scheme amount for 99.5% and 98.95% for both datasets, respectively, which suggest that our method decently stands out against reference state-of-the-art methodologies. Furthermore, experiments conclude that the presented scheme manifests a promising robustness under large-scale occlusion scenarios. (C) 2016 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： physiological biometrics ear imaging sparse coding feature fusion signal reconstruction

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Image classification by semisupervised sparse coding with confident unlabeled samples

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JOURNAL OF ELECTRONIC IMAGING 2017年第5期26卷 053013-053013页

作者： Li, Xiao Fang, Min Wu, Jinqiao He, Liang Tian, Xian Xidian Univ Sch Comp Sci & Technol Xian Shaanxi Peoples R China

sparse coding has achieved very excellent performance in image classification tasks, especially when the supervision information is incorporated into the dictionary learning process. However, there is a large amount of unlabeled samples that are expensive and boring to annotate. We propose an image classification algorithm by semisupervised sparse coding with confident unlabeled samples. In order to make the learnt sparse coding more discriminative, we select and annotate some confident unlabeled samples. A minimization model is developed in which the reconstruction error of the labeled, the selected unlabeled and the remaining unlabeled data and the classification error are integrated, which enhances the discriminant property of the dictionary and sparse representations. The experimental results on image classification tasks demonstrate that our algorithm can significantly improve the image classification performance. (C) 2017 SPIE and IS&T

关键词： image classification confident unlabeled samples sparse coding semisupervised learning multiclass linear classifier

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Combining sparse coding and time-domain features for heart sound classification

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PHYSIOLOGICAL MEASUREMENT 2017年第8期38卷 1701-1713页

作者： Whitaker, Bradley M. Suresha, Pradyumna B. Liu, Chengyu Clifford, Gari D. Anderson, David V. Georgia Inst Technol Dept Elect & Comp Engn Atlanta GA 30332 USA Emory Univ Dept Biomed Informat Atlanta GA 30322 USA Georgia Inst Technol Dept Biomed Engn Atlanta GA 30332 USA

Objective: This paper builds upon work submitted as part of the 2016 PhysioNet/CinC Challenge, which used sparse coding as a feature extraction tool on audio PCG data for heart sound classification. Approach: In sparse coding, preprocessed data is decomposed into a dictionary matrix and a sparse coefficient matrix. The dictionary matrix represents statistically important features of the audio segments. The sparse coefficient matrix is a mapping that represents which features are used by each segment. Working in the sparse domain, we train support vector machines (SVMs) for each audio segment (S1, systole, S2, diastole) and the full cardiac cycle. We train a sixth SVM to combine the results from the preliminary SVMs into a single binary label for the entire PCG recording. In addition to classifying heart sounds using sparse coding, this paper presents two novel modifications. The first uses a matrix norm in the dictionary update step of sparse coding to encourage the dictionary to learn discriminating features from the abnormal heart recordings. The second combines the sparse coding features with time-domain features in the final SVM stage. Main results: The original algorithm submitted to the challenge achieved a cross-validated mean accuracy (MAcc) score of 0.8652 (Se = 0.8669 and Sp = 0.8634). After incorporating the modifications new to this paper, we report an improved cross-validated MAcc of 0.8926 (Se = 0.9007 and Sp = 0.8845). Significance: Our results show that sparse coding is an effective way to define spectral features of the cardiac cycle and its sub-cycles for the purpose of classification. In addition, we demonstrate that sparse coding can be combined with additional feature extraction methods to improve classification accuracy.

关键词： heart sound heart sound classification sparse coding dictionary learning PhysioNet/CinC Challenge

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Hierarchical-Based Object Detection with Improved Locality sparse coding

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Chinese Journal of Electronics 2016年第2期25卷 290-295页

作者： ZHU Suguo DU Junping REN Nan LIANG Meiyu Beijing Key Laboratory of Intelligent Telecommunication software and Multimedia School of Computer ScienceBeijing University of Posts and Telecommunications

This paper proposes to extend the hierarchical method to be adapted to sequential frames, aiming at detecting the moving object in dynamic scenes. A novel two-layer model is proposed, in which dictionaries are learned through three different stages and the locality constrained sparse representation is improved. This leads more significant improvement for performance of both static image classification and moving object detection. The experimental results demonstrate that the proposed algorithm is efficient and robust compared with the state-of-the-art classification methods, and also able to detect moving object in the sequential frames accurately.

关键词： Hierarchical detection sparse coding Classification Object detection

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Multi-Hypergraph Consistent sparse coding

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ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 2017年第6期8卷 75-75页

作者： Feng, Xiaodong Wu, Sen Zhou, Wenjun Univ Elect Sci & Technol China Sch Polit Sci & Publ Adm 2006 Xiyuan Rd Chengdu 611731 Sichuan Peoples R China Univ Sci & Technol Beijing Donlinks Sch Econ & Management 30 Xueyuan Rd Beijing 100083 Peoples R China Univ Tennessee Business Analyt & Stat 800 Andy Hotly Tower Knoxville TN 37996 USA

sparse representation has been a powerful technique for modeling high-dimensional data. As an unsupervised technique to extract sparse representations, sparse coding encodes the original data into a new sparse code space and simultaneously learns a dictionary representing high-level semantics. Existing methods have considered local manifold within high-dimensional data using graph/hypergraph Laplacian regularization, and more from the manifold could be utilized to improve the performance. In this article, we propose to further regulate the sparse coding so that the learned sparse codes can well reconstruct the hypergraph structure. In particular, we add a novel hypergraph consistency regularization term (HC) by minimizing the reconstruction error of the hypergraph incidence or weight matrix. Moreover, we extend the HC term to multi-hypergraph consistent sparse coding (MultiCSC) and automatically select the optimal manifold structure under the multi-hypergraph learning framework. We show that the optimization of MultiCSC can be solved efficiently, and that several existing sparse coding methods can fit into the general framework of MultiCSC as special cases. As a case study, hypergraph incidence consistent sparse coding is applied to perform semi-auto image tagging, demonstrating the effectiveness of hypergraph consistency regulation. We perform further experiments using MultiCSC for image clustering, which outperforms a number of baselines.

关键词： sparse coding hypergraph consistency multiple-hypergraph learning image tagging image clustering

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